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Noise-Transfer2Clean: denoising cryo-EM images based on noise modeling and transfer
MOTIVATION: Cryo-electron microscopy (cryo-EM) is a widely used technology for ultrastructure determination, which constructs the 3D structures of protein and macromolecular complex from a set of 2D micrographs. However, limited by the electron beam dose, the micrographs in cryo-EM generally suffer...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8963287/ https://www.ncbi.nlm.nih.gov/pubmed/35134862 http://dx.doi.org/10.1093/bioinformatics/btac052 |
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author | Li, Hongjia Zhang, Hui Wan, Xiaohua Yang, Zhidong Li, Chengmin Li, Jintao Han, Renmin Zhu, Ping Zhang, Fa |
author_facet | Li, Hongjia Zhang, Hui Wan, Xiaohua Yang, Zhidong Li, Chengmin Li, Jintao Han, Renmin Zhu, Ping Zhang, Fa |
author_sort | Li, Hongjia |
collection | PubMed |
description | MOTIVATION: Cryo-electron microscopy (cryo-EM) is a widely used technology for ultrastructure determination, which constructs the 3D structures of protein and macromolecular complex from a set of 2D micrographs. However, limited by the electron beam dose, the micrographs in cryo-EM generally suffer from the extremely low signal-to-noise ratio (SNR), which hampers the efficiency and effectiveness of downstream analysis. Especially, the noise in cryo-EM is not simple additive or multiplicative noise whose statistical characteristics are quite different from the ones in natural image, extremely shackling the performance of conventional denoising methods. RESULTS: Here, we introduce the Noise-Transfer2Clean (NT2C), a denoising deep neural network (DNN) for cryo-EM to enhance image contrast and restore specimen signal, whose main idea is to improve the denoising performance by correctly learning the noise distribution of cryo-EM images and transferring the statistical nature of noise into the denoiser. Especially, to cope with the complex noise model in cryo-EM, we design a contrast-guided noise and signal re-weighted algorithm to achieve clean-noisy data synthesis and data augmentation, making our method authentically achieve signal restoration based on noise’s true properties. Our work verifies the feasibility of denoising based on mining the complex cryo-EM noise patterns directly from the noise patches. Comprehensive experimental results on simulated datasets and real datasets show that NT2C achieved a notable improvement in image denoising, especially in background noise removal, compared with the commonly used methods. Moreover, a case study on the real dataset demonstrates that NT2C can greatly alleviate the obstacles caused by the SNR to particle picking and simplify the identifying of particles. AVAILABILITYAND IMPLEMENTATION: The code is available at https://github.com/Lihongjia-ict/NoiseTransfer2Clean/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-8963287 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-89632872022-03-29 Noise-Transfer2Clean: denoising cryo-EM images based on noise modeling and transfer Li, Hongjia Zhang, Hui Wan, Xiaohua Yang, Zhidong Li, Chengmin Li, Jintao Han, Renmin Zhu, Ping Zhang, Fa Bioinformatics Original Papers MOTIVATION: Cryo-electron microscopy (cryo-EM) is a widely used technology for ultrastructure determination, which constructs the 3D structures of protein and macromolecular complex from a set of 2D micrographs. However, limited by the electron beam dose, the micrographs in cryo-EM generally suffer from the extremely low signal-to-noise ratio (SNR), which hampers the efficiency and effectiveness of downstream analysis. Especially, the noise in cryo-EM is not simple additive or multiplicative noise whose statistical characteristics are quite different from the ones in natural image, extremely shackling the performance of conventional denoising methods. RESULTS: Here, we introduce the Noise-Transfer2Clean (NT2C), a denoising deep neural network (DNN) for cryo-EM to enhance image contrast and restore specimen signal, whose main idea is to improve the denoising performance by correctly learning the noise distribution of cryo-EM images and transferring the statistical nature of noise into the denoiser. Especially, to cope with the complex noise model in cryo-EM, we design a contrast-guided noise and signal re-weighted algorithm to achieve clean-noisy data synthesis and data augmentation, making our method authentically achieve signal restoration based on noise’s true properties. Our work verifies the feasibility of denoising based on mining the complex cryo-EM noise patterns directly from the noise patches. Comprehensive experimental results on simulated datasets and real datasets show that NT2C achieved a notable improvement in image denoising, especially in background noise removal, compared with the commonly used methods. Moreover, a case study on the real dataset demonstrates that NT2C can greatly alleviate the obstacles caused by the SNR to particle picking and simplify the identifying of particles. AVAILABILITYAND IMPLEMENTATION: The code is available at https://github.com/Lihongjia-ict/NoiseTransfer2Clean/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2022-02-04 /pmc/articles/PMC8963287/ /pubmed/35134862 http://dx.doi.org/10.1093/bioinformatics/btac052 Text en © The Author(s) 2022. Published by Oxford University Press. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Original Papers Li, Hongjia Zhang, Hui Wan, Xiaohua Yang, Zhidong Li, Chengmin Li, Jintao Han, Renmin Zhu, Ping Zhang, Fa Noise-Transfer2Clean: denoising cryo-EM images based on noise modeling and transfer |
title | Noise-Transfer2Clean: denoising cryo-EM images based on noise modeling and transfer |
title_full | Noise-Transfer2Clean: denoising cryo-EM images based on noise modeling and transfer |
title_fullStr | Noise-Transfer2Clean: denoising cryo-EM images based on noise modeling and transfer |
title_full_unstemmed | Noise-Transfer2Clean: denoising cryo-EM images based on noise modeling and transfer |
title_short | Noise-Transfer2Clean: denoising cryo-EM images based on noise modeling and transfer |
title_sort | noise-transfer2clean: denoising cryo-em images based on noise modeling and transfer |
topic | Original Papers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8963287/ https://www.ncbi.nlm.nih.gov/pubmed/35134862 http://dx.doi.org/10.1093/bioinformatics/btac052 |
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